{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will to show how to use linear model stochastic gradient descent on multi-class classification/discrimination\n",
    "\n",
    "import class sklearn.linear_model.SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some functions to help us on preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear string\n",
    "def clearstring(string):\n",
    "    string = re.sub('[^A-Za-z ]+', '', string)\n",
    "    string = string.split(' ')\n",
    "    string = filter(None, string)\n",
    "    string = [y.strip() for y in string]\n",
    "    string = ' '.join(string)\n",
    "    return string.lower()\n",
    "\n",
    "# because of sklean.datasets read a document as a single element\n",
    "# so we want to split based on new line\n",
    "def separate_dataset(trainset):\n",
    "    datastring = []\n",
    "    datatarget = []\n",
    "    for i in range(len(trainset.data)):\n",
    "        data_ = trainset.data[i].split('\\n')\n",
    "        # python3, if python2, just remove list()\n",
    "        data_ = list(filter(None, data_))\n",
    "        for n in range(len(data_)):\n",
    "            data_[n] = clearstring(data_[n])\n",
    "        datastring += data_\n",
    "        for n in range(len(data_)):\n",
    "            datatarget.append(trainset.target[i])\n",
    "    return datastring, datatarget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
      "416809\n",
      "416809\n"
     ]
    }
   ],
   "source": [
    "# you can change any encoding type\n",
    "trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')\n",
    "trainset.data, trainset.target = separate_dataset(trainset)\n",
    "print (trainset.target_names)\n",
    "print (len(trainset.data))\n",
    "print (len(trainset.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bow = TfidfVectorizer(min_df=10).fit(trainset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out = bow.transform(trainset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset.target = np.array(trainset.target)\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(out, trainset.target, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until validation_1 error hasn't decreased in 20 rounds.\n",
      "[0]\tvalidation_0-mlogloss:1.748563\tvalidation_1-mlogloss:1.748771\n",
      "[1]\tvalidation_0-mlogloss:1.708156\tvalidation_1-mlogloss:1.708515\n",
      "[2]\tvalidation_0-mlogloss:1.670217\tvalidation_1-mlogloss:1.670610\n",
      "[3]\tvalidation_0-mlogloss:1.634315\tvalidation_1-mlogloss:1.634714\n",
      "[4]\tvalidation_0-mlogloss:1.603540\tvalidation_1-mlogloss:1.603368\n",
      "[5]\tvalidation_0-mlogloss:1.571127\tvalidation_1-mlogloss:1.571520\n",
      "[6]\tvalidation_0-mlogloss:1.541245\tvalidation_1-mlogloss:1.542299\n",
      "[7]\tvalidation_0-mlogloss:1.513506\tvalidation_1-mlogloss:1.514591\n",
      "[8]\tvalidation_0-mlogloss:1.487730\tvalidation_1-mlogloss:1.489031\n",
      "[9]\tvalidation_0-mlogloss:1.461161\tvalidation_1-mlogloss:1.462647\n",
      "[10]\tvalidation_0-mlogloss:1.438024\tvalidation_1-mlogloss:1.439336\n",
      "[11]\tvalidation_0-mlogloss:1.415658\tvalidation_1-mlogloss:1.416670\n",
      "[12]\tvalidation_0-mlogloss:1.392244\tvalidation_1-mlogloss:1.393468\n",
      "[13]\tvalidation_0-mlogloss:1.371884\tvalidation_1-mlogloss:1.373062\n",
      "[14]\tvalidation_0-mlogloss:1.352272\tvalidation_1-mlogloss:1.353763\n",
      "[15]\tvalidation_0-mlogloss:1.333361\tvalidation_1-mlogloss:1.334534\n",
      "[16]\tvalidation_0-mlogloss:1.315329\tvalidation_1-mlogloss:1.316637\n",
      "[17]\tvalidation_0-mlogloss:1.298889\tvalidation_1-mlogloss:1.300664\n",
      "[18]\tvalidation_0-mlogloss:1.281197\tvalidation_1-mlogloss:1.283310\n",
      "[19]\tvalidation_0-mlogloss:1.265109\tvalidation_1-mlogloss:1.267238\n",
      "[20]\tvalidation_0-mlogloss:1.249353\tvalidation_1-mlogloss:1.251585\n",
      "[21]\tvalidation_0-mlogloss:1.234478\tvalidation_1-mlogloss:1.236904\n",
      "[22]\tvalidation_0-mlogloss:1.221153\tvalidation_1-mlogloss:1.223740\n",
      "[23]\tvalidation_0-mlogloss:1.207482\tvalidation_1-mlogloss:1.210092\n",
      "[24]\tvalidation_0-mlogloss:1.194688\tvalidation_1-mlogloss:1.197506\n",
      "[25]\tvalidation_0-mlogloss:1.182619\tvalidation_1-mlogloss:1.185462\n",
      "[26]\tvalidation_0-mlogloss:1.170772\tvalidation_1-mlogloss:1.173746\n",
      "[27]\tvalidation_0-mlogloss:1.158778\tvalidation_1-mlogloss:1.161762\n",
      "[28]\tvalidation_0-mlogloss:1.146856\tvalidation_1-mlogloss:1.150299\n",
      "[29]\tvalidation_0-mlogloss:1.135148\tvalidation_1-mlogloss:1.138598\n",
      "[30]\tvalidation_0-mlogloss:1.123143\tvalidation_1-mlogloss:1.126771\n",
      "[31]\tvalidation_0-mlogloss:1.112406\tvalidation_1-mlogloss:1.116323\n",
      "[32]\tvalidation_0-mlogloss:1.102117\tvalidation_1-mlogloss:1.106240\n",
      "[33]\tvalidation_0-mlogloss:1.091937\tvalidation_1-mlogloss:1.096228\n",
      "[34]\tvalidation_0-mlogloss:1.081693\tvalidation_1-mlogloss:1.085779\n",
      "[35]\tvalidation_0-mlogloss:1.071931\tvalidation_1-mlogloss:1.076321\n",
      "[36]\tvalidation_0-mlogloss:1.062900\tvalidation_1-mlogloss:1.067197\n",
      "[37]\tvalidation_0-mlogloss:1.054608\tvalidation_1-mlogloss:1.058853\n",
      "[38]\tvalidation_0-mlogloss:1.044818\tvalidation_1-mlogloss:1.049145\n",
      "[39]\tvalidation_0-mlogloss:1.036809\tvalidation_1-mlogloss:1.041202\n",
      "[40]\tvalidation_0-mlogloss:1.027683\tvalidation_1-mlogloss:1.032354\n",
      "[41]\tvalidation_0-mlogloss:1.019692\tvalidation_1-mlogloss:1.024559\n",
      "[42]\tvalidation_0-mlogloss:1.011114\tvalidation_1-mlogloss:1.016170\n",
      "[43]\tvalidation_0-mlogloss:1.003417\tvalidation_1-mlogloss:1.008579\n",
      "[44]\tvalidation_0-mlogloss:0.995416\tvalidation_1-mlogloss:1.000847\n",
      "[45]\tvalidation_0-mlogloss:0.987254\tvalidation_1-mlogloss:0.992761\n",
      "[46]\tvalidation_0-mlogloss:0.979453\tvalidation_1-mlogloss:0.985072\n",
      "[47]\tvalidation_0-mlogloss:0.972560\tvalidation_1-mlogloss:0.978227\n",
      "[48]\tvalidation_0-mlogloss:0.965621\tvalidation_1-mlogloss:0.971293\n",
      "[49]\tvalidation_0-mlogloss:0.958145\tvalidation_1-mlogloss:0.963785\n",
      "[50]\tvalidation_0-mlogloss:0.951630\tvalidation_1-mlogloss:0.957370\n",
      "[51]\tvalidation_0-mlogloss:0.945156\tvalidation_1-mlogloss:0.951071\n",
      "[52]\tvalidation_0-mlogloss:0.938306\tvalidation_1-mlogloss:0.944346\n",
      "[53]\tvalidation_0-mlogloss:0.931456\tvalidation_1-mlogloss:0.937562\n",
      "[54]\tvalidation_0-mlogloss:0.925251\tvalidation_1-mlogloss:0.931365\n",
      "[55]\tvalidation_0-mlogloss:0.919391\tvalidation_1-mlogloss:0.925584\n",
      "[56]\tvalidation_0-mlogloss:0.913294\tvalidation_1-mlogloss:0.919585\n",
      "[57]\tvalidation_0-mlogloss:0.907314\tvalidation_1-mlogloss:0.913694\n",
      "[58]\tvalidation_0-mlogloss:0.901539\tvalidation_1-mlogloss:0.908041\n",
      "[59]\tvalidation_0-mlogloss:0.896016\tvalidation_1-mlogloss:0.902627\n",
      "[60]\tvalidation_0-mlogloss:0.890066\tvalidation_1-mlogloss:0.896767\n",
      "[61]\tvalidation_0-mlogloss:0.884661\tvalidation_1-mlogloss:0.891515\n",
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      "[64]\tvalidation_0-mlogloss:0.867722\tvalidation_1-mlogloss:0.874568\n",
      "[65]\tvalidation_0-mlogloss:0.862119\tvalidation_1-mlogloss:0.868992\n",
      "[66]\tvalidation_0-mlogloss:0.857118\tvalidation_1-mlogloss:0.863921\n",
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      "[69]\tvalidation_0-mlogloss:0.842021\tvalidation_1-mlogloss:0.849292\n",
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      "[75]\tvalidation_0-mlogloss:0.812384\tvalidation_1-mlogloss:0.820154\n",
      "[76]\tvalidation_0-mlogloss:0.807473\tvalidation_1-mlogloss:0.815369\n",
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      "[78]\tvalidation_0-mlogloss:0.798473\tvalidation_1-mlogloss:0.806518\n",
      "[79]\tvalidation_0-mlogloss:0.793908\tvalidation_1-mlogloss:0.801898\n",
      "[80]\tvalidation_0-mlogloss:0.789640\tvalidation_1-mlogloss:0.797733\n",
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      "[102]\tvalidation_0-mlogloss:0.705703\tvalidation_1-mlogloss:0.715429\n",
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      "[105]\tvalidation_0-mlogloss:0.695722\tvalidation_1-mlogloss:0.705690\n",
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      "[110]\tvalidation_0-mlogloss:0.679992\tvalidation_1-mlogloss:0.690293\n",
      "[111]\tvalidation_0-mlogloss:0.676925\tvalidation_1-mlogloss:0.687209\n",
      "[112]\tvalidation_0-mlogloss:0.673838\tvalidation_1-mlogloss:0.684245\n",
      "[113]\tvalidation_0-mlogloss:0.670892\tvalidation_1-mlogloss:0.681367\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[122]\tvalidation_0-mlogloss:0.645132\tvalidation_1-mlogloss:0.656173\n",
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      "[1662]\tvalidation_0-mlogloss:0.151537\tvalidation_1-mlogloss:0.220505\n",
      "[1663]\tvalidation_0-mlogloss:0.151501\tvalidation_1-mlogloss:0.220507\n",
      "[1664]\tvalidation_0-mlogloss:0.151464\tvalidation_1-mlogloss:0.220518\n",
      "[1665]\tvalidation_0-mlogloss:0.151436\tvalidation_1-mlogloss:0.220516\n",
      "[1666]\tvalidation_0-mlogloss:0.151401\tvalidation_1-mlogloss:0.220524\n",
      "[1667]\tvalidation_0-mlogloss:0.151373\tvalidation_1-mlogloss:0.220520\n",
      "[1668]\tvalidation_0-mlogloss:0.151344\tvalidation_1-mlogloss:0.220512\n",
      "[1669]\tvalidation_0-mlogloss:0.151317\tvalidation_1-mlogloss:0.220510\n",
      "[1670]\tvalidation_0-mlogloss:0.151282\tvalidation_1-mlogloss:0.220525\n",
      "[1671]\tvalidation_0-mlogloss:0.151254\tvalidation_1-mlogloss:0.220540\n",
      "[1672]\tvalidation_0-mlogloss:0.151217\tvalidation_1-mlogloss:0.220538\n",
      "[1673]\tvalidation_0-mlogloss:0.151193\tvalidation_1-mlogloss:0.220538\n",
      "[1674]\tvalidation_0-mlogloss:0.151166\tvalidation_1-mlogloss:0.220540\n",
      "Stopping. Best iteration:\n",
      "[1654]\tvalidation_0-mlogloss:0.151781\tvalidation_1-mlogloss:0.220483\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.4,\n",
       "       gamma=0.65, learning_rate=0.1, max_delta_step=1.8, max_depth=7,\n",
       "       min_child_weight=10.0, missing=None, n_estimators=10000, nthread=-1,\n",
       "       objective='multi:softprob', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=False, subsample=0.8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_xgd = {\n",
    "    'min_child_weight': 10.0,\n",
    "    'max_depth': 7,\n",
    "    'objective': 'multi:softprob',\n",
    "    'max_delta_step': 1.8,\n",
    "    'colsample_bytree': 0.4,\n",
    "    'subsample': 0.8,\n",
    "    'learning_rate': 0.1,\n",
    "    'gamma': 0.65,\n",
    "    'nthread': -1,\n",
    "    'silent': False,\n",
    "    'n_estimators': 10000\n",
    "    }\n",
    "clf = xgb.XGBClassifier(**params_xgd)\n",
    "clf.fit(train_X,train_Y, eval_set=[(train_X,train_Y), (test_X,test_Y)], \n",
    "        eval_metric='mlogloss', early_stopping_rounds=20, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy validation set:  0.885415417097\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "      anger       0.88      0.90      0.89     11414\n",
      "       fear       0.81      0.83      0.82      9584\n",
      "        joy       0.92      0.91      0.91     28269\n",
      "       love       0.74      0.77      0.75      6878\n",
      "    sadness       0.95      0.93      0.94     24196\n",
      "   surprise       0.66      0.67      0.67      3021\n",
      "\n",
      "avg / total       0.89      0.89      0.89     83362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "predicted = clf.predict(test_X)\n",
    "print('accuracy validation set: ', np.mean(predicted == test_Y))\n",
    "\n",
    "# print scores\n",
    "print(metrics.classification_report(test_Y, predicted, target_names = trainset.target_names))"
   ]
  }
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